The Impacts of the American-Chinese Trade War and COVID-19 Pandemic on Taiwan’s Sales in Semiconductor Industry
Bibliographic record
Abstract
The following paper deals with the American Chinese trade war and its impacts on Taiwan’s economy, particularly sales in Taiwan’s semiconductor industry. Indeed, trade tensions impact global supply chains, especially in the semiconductor industry, since its supply chain is highly globalized and dependent on many companies in various countries. Hence, the industry is susceptible to trade disruptions. With the largest microchip manufacturer TSMC, Taiwan is one of the key players in the fabrication of microchips. It has strong cultural, geographical, and economic ties to China and, on the other hand, strong economic and military relations to the United States. A trade war between those two countries is an enormous future challenge for the island. However, this paper proves that trade tensions had a lower-than-expected impact on Taiwan’s economy and the microchip industry. Due to capital that diverted from China to Taiwan and investments from Taiwanese companies in other countries like the USA. Additionally, Taiwan handled the Covid-19 pandemic extraordinarily well and therefore did not have any significant economic restrictions in the domestic market. Now it depends on the future action steps of the Taiwanese industry and government. If Taiwan manages to steer outgoing companies from China to Taiwan, the island could emerge as the surprise winner of the trade dispute. For this purpose, the paper gives concrete recommendations on how to increase the attractiveness for FDI through tax benefits or infrastructure investments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".